1. Technical Field
This invention relates to farming and, more particularly, to apparatus and methods for producing georeferenced agricultural maps of farming fields and for analyzing the maps to match farm inputs, such as chemicals and water, to current soil and vegetation characteristics to optimize productivity of the farming field.
2. Discussion
Productivity of agricultural lands has a major impact on the world economy and, as world population increases, this impact will increase substantially. Over the last four decades, productivity has doubled while labor has been reduced by a factor of three. Most of the improved productivity and reduced labor can be attributed to advances in irrigation and harvesting machinery and in improved fertilizing and insecticide chemicals.
Specific soil characteristics can vary significantly within a farming field. Particular regions of the farming field can receive too much or too little fertilizer, water and/or insecticide. Environmental damage can occur due to excess inputs and sub-optimal crop yield can occur in farming regions receiving insufficient farming inputs.
Precision farming has been proposed to provide farming inputs which are varied to match specific soil characteristics of each region of a farming field to prevent environmental damage and to allow crop yield optimization. Conventional chemical spreading machinery can currently spread chemicals at variable rates based upon an input soil map. Similarly, conventional irrigation systems allow chemicals and water to be controlled and varied over time and location based upon an input soil map. However, conventional prescription farming approaches are limited with respect to the type and/or extent of data provided. In other words, current approaches do not generate sufficiently competent data for incorporation into the input soil maps which control the chemical spreading machinery and the irrigation systems.
One approach generates soil maps from aerial photographs. While the photographs provides some indication of soil conditions, little or no information relating to crop development and/or yield is provided. Spectral image data provided is limited and difficult to correlate to quantifiable conditions.
A second approach generates soil maps from manual measurements made using soil probes. The second approach also provide some indication of soil conditions but does not provide information relating to crop development and/or yield. The second approach is labor intensive and provides limited or discrete data samples which do not adequately represent soil and vegetation characteristics.
A third approach, related to the second approach, generates soil maps from "on-the-move" soil probes. While the third approach is more comprehensive than the second approach, similar disadvantages are present. The third approach provides some indication of soil conditions but does not provide information relating to crop development and/or yield. The third approach is similarly labor intensive and provides limited or discrete data samples which, as above, do not adequately represent soil and vegetation characteristics.
A fourth approach generates spectral images or maps from airborne or spaceborne multispectral sensors which generate spectral signals related to soil type and crop stress. Vegetation indices are calculated from the spectral signals generated from a limited number of spectral bands, typically less than four spectral bands.
Data generated using the fourth approach has not been used for generating input soil maps for chemical spreading machinery and/or irrigation systems because the spectral images are not georeferenced and therefore do not provide sufficient precision. The vegetation indices calculated from the spectral signals are not calibrated with nutrient contents of the soil and/or vegetation. Calibrating spectral imagery generated by the fourth approach from day to day has been difficult since the magnitude of spectral reflectance is related to light intensity and atmospheric conditions. As a result, databases which summarize soil and vegetation characteristics and other related data for the farming field have been either unobtainable or imprecise.
Therefore, a precision farming system addressing the above problems is desired.